|In recent years, companies have faced great customer demand change involving, for example, a change in colour (aesthetic design), shape (measurement) or technical characteristics of some components (functionality) (Piller, 2005) of the products that companies assemble in their assembly systems (ASs) (Battaïa et al., 2018). Hence, if companies decide to accept those requests from their customers, they must be ready to produce more complex and diverse products (Otto and Li, 2020). The greater the complexity and diversity of the products that companies need to assemble, the greater the chance that ASs will fail to achieve their goals. Here the goal would be to have high efficiency, maximising throughput with minimum resources, to have high flexibility, rapid changes in the production volume and type of products to be assembled, to minimise the work in progress (WIP), assemble high-quality products, minimise the inventory level, utilise the workforce effectively and to minimise the disruptions in production (Bukchin and Masin, 2004; Hu et al., 2011; Vallandingham et al., 2017) and, thus, satisfying the demand. Therefore, companies are required to find ways that can enable them to handle this complex and diverse demand while achieving and maintaining their AS goals. One way might be the implementation of Industry 4.0 (I4.0) technologies (e.g., Internet of Things (IoT) technologies, data analysis, cloud computing, collaborative robots (cobots), augmented reality (AR), virtual reality (VR) and mobile robots, etc.) in the ASs, creating the so-called Assembly System 4.0 (AS4.0) (Bortolini et al., 2017; Dolgui et al., 2022). Although I4.0 technologies promise increased flexibility, better quality and improved productivity of ASs (Zhong et al., 2017), if they are not implemented correctly, they may cause risk for the ASs performance. This especially happens when the companies do not know how to design and manage AS4.0. In fact, although companies should know how to design and manage a traditional AS where no I4.0 technologies are implemented (Battini et al., 2011), there are no clear roadmap on how to design and manage AS4.0 (Dolgui et al., 2022). Therefore, the main goal of the present research study is to support and create new knowledge for the academy and managers and practitioners who would like to design and manage AS4.0.
We start the current research by conducting a systematic literature review (SLR). The SLR work give the opportunity of not only understanding the state of the art of AS4.0, but also the opportunity of identifying the nine future research opportunities concern the introduction of methods (“a method is a systematic approach to achieve a specific result or goal and offers a description in a cohesive and (scientific) consistent way of the approach that leads to the desired result/ goal.” (Verbrugge, 2019)) and models (“a model is the presentation in schematic form, often in a simplified way, of an existing or future state or situation” (Verbrugge, 2019)) for the design and management of AS4.0. Examples of methods are descriptive, rational, and experimental (e.g., observations, framework, and decision support systems), while example of models are graphs, flow charts, 3D models, diagrams, and equations. The nine future research opportunities are models of dynamic reconfigurable AS4.0; models to support the selection of a suitable level of automation; models for dynamic assignment of technologies on AS4.0 configurations; methods for ergo-efficient workplace design; models of new feeding policies; models for real-time multiobjective balancing of assembly line subsystem (ALS) and the scheduling of assembly line feeding subsystem (ALFS); smart and real-time methods for sequencing of ALS and routing of ALFS; methods for efficient control of AS4.0; and models and methods for maintenance of technologies (Dolgui et al., 2022). Based on three of those future research opportunities (i.e., models of dynamic reconfigurable AS4.0, methods for ergo-efficient workplace design and methods for efficient control of AS4.0), we derive the below three research questions:
• RQ1: How can AS4.0 configurations be affected by Industry 4.0 technologies?
• RQ2: How can ergo-efficient AS4.0 workplaces be designed?
• RQ3: How can material management be controlled in AS4.0?
To answer these research questions, the below methods are applied:
• RQ1: exploratory research and experimental research
• RQ2: experimental research
• RQ3: simulation modelling
The main outcomes are derived by answering the three research questions by using those methods:
Factors for the modelling of dynamic reconfigurable configurations of an Assembly Line Subsystem and the impact of Industry 4.0 technologies on these factors o Identification and definition of the factors relevant for modelling dynamic reconfigurable configurations of an ALS and identification of how Industry 4.0 technologies impact those factors. o Investigation of the impact of augmented reality on the identified factors.
A framework to design the workplace of an Assembly Line Subsystem by using Virtual Reality and a motion capture system o Identification of the steps to design an ergo-efficient workplace of an ALS by using VR and a motion capture (mocap) system. o Identification of how the age of human workers can be included in the design of a workplace of an ALS.
A Decision Support System to select the best material management solution among traditional Kanban, electronic Kanban, and Digital Twin o Development of a digital twin (DT)-based model for the control of the material management in an ALFS. o Identification of the best solution among traditional Kanban, electronic Kanban and DT to control the material management in an ALFS.
Finally, by the outcome of the SLR and research questions, we reach our main goals that are:
“support and create new knowledge for academy and managers and practitioners who would like to design and manage AS4.0”. In fact, by the SLR work, we aim to present the state of the art and possible future research opportunities related to the design and management of AS4.0. Also, we aim to support companies implementing I4.0 technologies in their ASs, showing them the decisions that they have to make, level by level (strategic, tactical and operational), for each technology if they want to achieve and maintain the goals of the ASs. In answering RQ1, we aim to provide companies information during the design phase of their ASs, explaining to them the impact that each I4.0 technology has on those factors relevant to the decision of which AS configuration to choose. With the answer to RQ2, we aim to encourage companies to use VR when designing the workplaces in their ASs. In fact, they can use our five-step methodological framework, which considers the age of the operators in designing ergo-efficient AS workplaces, here by using VR together with a motion capture system. Finally, based on the answer to RQ3, we aim to support companies in deciding which solution to choose to control the material management activity in their ASs. In fact, we provide a decision support system (DSS) by which companies can choose the most convenient material management solution, here based on investment and operational costs, among traditional Kanban, e-Kanban and DT-based solutions, by considering the demand and layout parameters of their ASs